Efficient locomotion controllers are crucial for legged robots to achieve long battery life and extended operational time in the wild.
Quadrupedal animals demonstrate a wide variety of distinctive locomotion patterns, from slow pacing to fast galloping, which are known as gaits.
Studies [1]} have shown that animals in nature switch gaits at different speeds to lower their energy consumption.
In this paper, inspired by this finding, we aim to develop energy-efficient controllers for quadrupedal robots through reinforcement learning.
We show that distinctive locomotion gaits can emerge automatically with a simple reward function tied to energy minimization.
In addition, by learning a policy through our proposed reinforcement learning framework, gait transitions can occur naturally when the robot changes its speed.
